Upper respiratory tract microbiota is associated with small airway function and asthma severity

Background Characteristics of airway microbiota might influence asthma status or asthma phenotype. Identifying the airway microbiome can help to investigate its role in the development of asthma phenotypes or small airway function. Methods Bacterial microbiota profiles were analyzed in induced sputum from 31 asthma patients and 12 healthy individuals from Beijing, China. Associations between small airway function and airway microbiomes were examined. Results Composition of sputum microbiota significantly changed with small airway function in asthma patients. Two microbiome-driven clusters were identified and characterized by small airway function and taxa that had linear relationship with small airway functions were identified. Conclusions Our findings confirm that airway microbiota was associated with small airway function in asthma patients. Supplementary Information The online version contains supplementary material available at 10.1186/s12866-023-02757-5.


Background
Asthma is a heterogeneous disease characterized by inflammation and hyperresponsive in airways, which has several phenotypes and endotypes that may response differently to therapies. Despite important advances in asthma, including greater awareness, timely diagnosis, and pharmacological interventions targeted at airway inflammation, control of asthma in patients remains unsatisfactory.
A possible reason for poor asthma control might be that other than "Eosinophils asthma phenotype" or "Neutrophil asthma phenotype", some patients express a "small airways phenotype", which has small airways inflammation and dysfunction that is not being targeted or controlled by current therapies. The small airways are defined by an internal airway diameter of < 2 mm. They have a generation number that is generally higher than 8, and they account for 98.8% (approximately 4500 ml) of the total lung volume, compared to that the large airways account for only 1.2% (approximately 50 ml). Though inflammation and remodeling in asthma involve the large airways, the small airways are the major site of airflow limitation, and where the intensity of the inflammation may be even higher than that in large airways. Transbronchial biopsy findings show that small airways are the major site of inflammation and contain immunocytes that putatively account for the tissue remodeling noted [1,2]. Thus, small airways might affect the pathobiology of asthma and small airway dysfunction may contribute to poor asthma control [1][2][3][4], and the small airways of individuals with asthma are increasingly recognized as a potential therapeutic target [2,4,5].
The microbiota in human airways changes with disease. With the bacterial 16S ribosomal RNA gene sequencing technique, different microbiota were identified between asthma phenotypes, suggesting that microbial patterns in the airways may influence distinct phenotypes of asthma [6][7][8] and allergic inflammation [9]. Airway microbiota composition is also associated with the degree of airway hyperresponsiveness among patients with less controlled asthma. Indeed, several bacterial taxa, including Streptococcus pneumonia, Staphylococcus aureus, Moraxella catarrhalis, Pseudomonas aeruginosa, and Haemophilus influenza, were reported to be associated with asthma exacerbation or development [10,11]. Moreover, studies suggest that airway microbiome in asthma patients is probably a result of complex interactions between the inflammatory milieu and the drug effects, and microbial-derived mechanisms might be the reason of poor response to the treatment. For example, treatment with a combination of inhaled corticosteroids (ICSs) and oral glucocorticoids correlates positively with an increased abundance of Proteobacteria and Pseudomonas, and with a decreased abundance of Bacteroidetes, Fusobacteria, and Prevotella [12]. Meanwhile, a unique enrichment of Haemophilus, Neisseria, Fusobacterium, Porphyromonas species and the Sphingomonodaceae family along with depletion in Mogibacteriaceae and Lactobacillales was observed in mild asthma patients without being treated with ICSs [13].
In this study, the association between airway microbiota pattern and small airway function was explored. Results from lung function tests were related to the bacterial flora in study subject sputum.

Study population
All individuals with asthma were patients from the Respiratory Department in Chaoyang Hospital, Beijing, while 12 healthy individuals were recruited from routine physical examination department in the same institution. The age distribution of these healthy people were from 28 to 58 and they were ruled out of asthma and other respiratory diseases by scan examination and pulmonary function tests according to the Global Strategy for Asthma Management and Prevention [15,16].
Among the 31 individuals with asthma, we took a cut-off value of 65% for MEF25 pred% and MEF50 pred% to define study groups according to the Chinese Thoracic Society [17][18][19]. We defined patients who had a MEF25 pred% lower than 65% as the MEF 25pred% -low group (26 people), and others with a MEF 25pred% value higher than 65% as the MEF 25pred% -high group (5 people). The MEF50 pred% -low group and MEF50 pred% -high group were similarly defined, and 14 patients were grouped in the MEF50 pred% -high group versus 17 in the MEF50 pred% -low group.
As MEF50 and MEF25 are similar indices of small airway function, and because the sample size of the MEF50pred%-high group and MEF50 pred% -low group is closer than those of MEF25 pred% groups, we compared the sputum microbiome only between the MEF50 groups and the healthy individuals.
Subject characteristics are presented in Table 1.

Sampling of induced sputum
Induced sputum from asthma patients and health individuals was collected according to standardized protocols [20,21]. Study subjects were pre-treated with inhaled salbutamol to relax airway smooth muscle and to prevent acute asthma attack. Then they inhaled a nebulized solution of 3% saline over a 2-minute period, spat out the saliva, took 2 deep inspirations of saline, and coughed sputum into a separate cup. This procedure was repeated for six times. Subjects were instructed to rinse orally with water and to blow their nose after each inhalation to avoid contamination with saliva and post-nasal drip. Sputum samples were collected into sterilized pots and stored at − 80 °C for bacterial DNA extraction. Peak flow is monitored throughout the procedure, if patients feel uncomfortable or symptoms occurred, the induction was stopped.

DNA extraction, PCR amplification and Illumina sequencing
Microbial DNA was extracted from induced sputum. The final DNA concentration and purification were determined by NanoDrop 2000 UV-vis spectrophotometer (Thermo Scientific, Wilmington, USA), and DNA quality was checked by 1% agarose gel electrophoresis. The Purified amplicons were pooled in equimolar and paired-end sequenced (2 × 300) on an Illumina MiSeq platform (Illumina, San Diego, USA) according to the standard protocols [22,23].

Bioinformatics analysis
The analysis was conducted by following the "Atacama soil microbiome tutorial" of Qiime2docs along with customized program scripts (https:// docs. qiime2. org/ 2019.1/). Briefly, raw data FASTQ files were imported into the format which could be operated by QIIME2 system using qiime tools import program. Demultiplexed sequences from each sample were quality filtered and trimmed, de-noised, merged, and then the chimeric sequences were identified and removed using the QIIME2 dada2 plugin to obtain the feature table of amplicon sequence variant (ASV). The QIIME2 featureclassifier plugin was then used to align ASV sequences to a pre-trained GREENGENES 13_8 99% database (trimmed to the V3V4 region bound by the 338F/806R primer pair) to generate the taxonomy table. Any Table 1 Clinical characteristics of study subjects a a Data are expressed as mean values and standard errors b counts in blood *: indicating a statistical significant difference between MEF25 pred% -high and MEF25 pred% -low groups, or between MEF50 pred% -high and MEF50 pred% -low groups. Significant level: *, p < 0.05; **: p < 0.01

Statistics and identification of bacterial communities
We used a rank test method, the Kruskal-Wallis test to examine the differences between groups. The linear Discriminant Analysis Effect Size (LEfSe) method [24] was employed to compare the bacterial composition between groups, with the cutoff p-value set as 0.05 (after Benjamini-Hochberg false discovery rate correction). Additionally, Kyoto Encyclopedia of Genes and Genomes (KEGG) functional profiles of microbial communities were predicted with Phylogenetic Reconstruction of Unobserved States (PICRUSt) [25].
No significant difference of blood eosinophils or serum IgE was observed between these two pairs of groups.

Sputum microbiome compositions
A total of 2,305,983 valid reads were generated for the 43 samples. After filtering for low-quality reads, 51,245 sequence reads were used for subsequent analyses and resulted in 12,265 OTUs. The average percentage of input passed filter was approximately 85%, and average percentage of input non-chimeric was approximately 77%.
We then and compared the difference in the median relative abundance of taxa between the healthy individuals and MEF50 pred% -low group, as the metagenomics phylogenetic map shows in Fig. 1D.
It could be seen from Fig. 1D that the largest significant (p < 0.05) difference in the median relative abundance of taxa was observed in the genus Prevotella, which was in accordance with the difference in microbiome composition. At the species level in this genus, significant (p < 0.05) difference was observed in species Prevotella nanceiensis (P. nanceiensis), Prevotella nigrescens (P. nigrescens), Prevotella copri (P. copri) and Prevotella pallens (P. pallens), and all these species had a relative abundance higher than 0.01% ( Supplementary  Fig. 1).
The second largest significant (p < 0.05) difference in the median relative abundance of taxa was observed in genus Streptococcus. At the species level in this genus, the relative abundance of Streptococcus infantis (S. infantis) was significantly different between MEF50 pred% -low group and healthy control group (Supplementary Fig. 1).

Partial least squares discriminant analysis (PLS-DA) of microbial difference
The PLS-DA model was established to identify the contribution of taxa to the difference in the community structure between the groups. Figure 2 shows the results of supervised PLS-DA plots concerning the microbial difference between MEF25 pred% and MEF50 pred% functional groups. It could be seen that the two clusters were characterized by composition difference according to MEF25 pred% ( Fig. 2A), MEF50 pred% (Fig. 2B) function and asthma severity.
Moreover, a heat map of the Euclidian distance of taxa between clusters characterized by MEF50 pred% function groups was shown in Fig. 3, indicating the distribution of taxa to component 1 in each sample.

Linear associations between sputum microbiome and small airway indices
Mixed multiple linear regression analysis (MaAslin) was performed to explore whether there was a linear relationship between sputum microbiomes with MEF25, MEF50, MEF75, PEF, MEF (75/25), and FEV 1 /FVC. Figure 4 shows the heat map of these significant (p < 0.05) estimates, indicating the magnitude of coefficients in the linear associations.
It could be seen that MEF (75/25) and FEV 1 /FVC had most associations with the microbiome. Only Fig. 1 The sputum microbiome at the genus level. A Bar plot of all the samples, each bar shows the relative abundance of one individual B) Pie chart of the microbiome composition at genus level in MEF50 pred% -low group. C Pie chart of the microbiome composition at genus level in healthy individuals. D Phylogenetic map of the median relative abundance differences in bacterial taxa between the healthy control group and the MEF50 pred% -low group, the ending circle of each branch represented for species (n = 29). The depth of color of the nodes corresponds to the degree of difference in median relative abundance of the bacterial taxa. The darker the color of the phylogenetic branches, the higher median differences, whereas gray nodes and branches indicate no significant differences

KEGG pathway analysis
16S rDNA amplicon data were supplemented with genomicdata using PICRUSt. Genes from different bacteria likely to perform the same function have been grouped into KEGG orthologues (KO) by the Kyoto Encyclopedia of Genes and Genomes (KEGG). Differentially abundant KOs were screened by using the Bonferroni-corrected Wilcoxon rank sum test for differences between healthy individuals and MEF50 pred% -low group. Significant (p < 0.05) differences are shown in Fig. 5. In the MEF25 pred% -low and MEF25 pred% -high groups, changes in the microbial flora of function genes in six categories were related to pathways associated with metabolism of cofactors and vitamins, transport and catabolism, biosynthesis and secondary metabolism, immune disease, and the endocrine system (Fig. 5A). For the MEF50 pred% -low and MEF50-high groups, changes in the function genes were in genes associated with energy and carbohydrate metabolism, replication and repair, protein folding, sorting and degradation, amino acid metabolism, drug resistance, xenobiotics, and infectious disease (Fig. 5B).

Discussion
In this study, we found significant differences in the composition, relative abundance, biomarkers and signaling pathways of airway microbiome between small airway functional groups and healthy controls. Two microbiome-driven clusters were identified and characterized by small airway function, and change in the microbiome composition between small airway functional group was observed. Our study gave evidence to the connection between respiratory tract microbiota and small airway function in asthma patients.
Although the precise role of bacterium in airway inflammation remains to be established, some genera or bacteria were reported to be associated with asthma severity and phenotype. Specifically, genera Haemophilus, Moraxella, and Neisseria of the phylum Proteobacteria, or species Haemophilus influenzae and Moraxella catarrha, were associated with worse asthma control [6,8,33,34]. In this study, we also found some associations between specific bacteria and small airway functions. First, we observed two species, P. pallens and P. nanceiensis, were correlated with better small airway function and better asthma status.
These two species had positive linear estimates with MEF50, MEF25, MEF (75/25) and FEV 1 /FVC. More than that, P. nanceiensis was a biomarker in the healthy control group ( Supplementary Fig. 2), and its relative abundance significantly (p < 0.05) decreased in small airway dysfunction groups (MEF25 pred% -low and MEF50 pred% -low groups); and it had the largest decreased fold-difference in MEF50 pred% -low group  Fig. 3). This is in accordance with those studies that reported P. nanceiensis as a "beneficial" commensal bacterium in respiratory system. In one of those studies, compared with healthy airways, abundance of P. nanceiensis decreased in the airways of patients with chronic obstructive pulmonary disease (COPD), asthma, diabetes, celiac disease, and chronic periodontitis [35,36]. In another study about children with Henoch-Schönlein Purpura [37], P. nanceiensis was observed to be positively correlated with IgA increase. IgA is important at mucosal surfaces for maintaining homeostasis [38,39], and it complexes activate eosinophils and neutrophils in inflammation. In this situation, P. nanceiensis might have participated in the immune responses. This is in accordance with earlier findings that increased P. nanceiensis was associated with diminished neutrophilic airway inflammation, suggesting that P. nanceiensis is related to Th2-high type asthma [40]. So it is possible that some commensal bacteria of the airways may participate in the regulation of local and distant immune responses [41].
We also observed that some taxa that had negative associations with small airway functions or enriched significantly (p < 0.05) in MEF25 pred% -low or MEF50pred%low group. Many of these taxa play a role in human lung, oral and cardiovascular diseases [42]. Among these taxa, C. rectus, which had the largest negative estimate with all small airway functional indices, was enriched significantly (p < 0.05) in MEF25 pred% -low and MEF50 pred% -low groups. C. rectus was reported to be associated with periodontal disease [43], and was linked to coronary artery disease, lung abscess, empyema, brain abscess, and osteomyelitis [43,44]. The precise reasons for these associations are unclear. However, evidence showed that C. rectus increased production of the proinflammatory cytokines IL-6 and IL-8 in human gingival fibroblasts [45], suggesting it may induce an inflammatory milieu in other tissues.
In this study, P. nigrescens was also observed to have a negative estimate with MEF (75/25) and FEV 1 /FVC. More recently, P. nigrescens was reported to be associated with signs of carotid atherosclerosis in patients without periodontitis and endodontic infections [46,47]. The later finding of dental colonization suggests possible distal spread of either the bacteria or inflammatory mediators such as cytokines. Still, patients with asthma show increased risk of bacterial infection. Certain bacterial species may transition from benign to pathogenic activities under some conditions but whether this is true in asthma requires additional research.
R. dentocariosa was the only taxa that had negative estimates with all small airway and lung functions observed in this study. R. dentocariosa is a normal commensal bacterium of the oral cavity and is associated with dental caries and periodontal disease. The bacterium is also reported to be associated with septic arthritis, pneumonia, arteriovenous infection, and acute bronchitis [48]. Of note, R. dentocariosa can upregulation production of TNF-a by T cells [49].
S. anginosus was another taxa observed in our study to have a negative relationship with small airway function and it has been reported to be associated with pharyngitis and infections of internal organs and certain body fluids [50].
Functional analysis using PICRUSt showed clear differences between the bacterial predicted metabolic functions in different study group in our work. Pathway analysis of changes in the microbial flora genes indicated that they were related to carbohydrate and amino acid metabolism, cellular processes, and human diseases, and that the changes were distributed in different proportions. These findings are in accordance with other reports and suggest increased metabolic activity of the airway microbiome in asthmatic individuals [51,52]. However, due to the limitation of PICRUSt, this prediction did not correspond to specific genera. Combining these analytic approaches may yield new insights.
The present study has a number of limitations. First, the cohort sample size is moderate and may not accurately reflect the true population. Second, some important indexes, such as IgA, were not tested for all patients with asthma. Further, the role of seasonal irritants, pollutants and smoke ingestion, such as from tobacco, was not tested in this study.
To sum up, our work gave evidence that small airway function was associated with respiratory tract microbiome, and commensal microorganisms may participate in the regulation of local and distant immune responses. Our findings could provide some information to therapy for patients with "small airway phenotype" asthma.